Many current image search systems are text-based, and depend on meta-data and tagging to provide search keys. One example of such a text-based image search system is the Google Image Search service, which allows users to search the web for image content. The keywords for Google Image Search are based on the filename of the image, the link text pointing to the image, and/or text adjacent to the image. While searching for an image, a thumbnail of each matching image is displayed.
Recently, a number of advances have been made with respect to computer vision research. Specifically, content-based image retrieval (CBIR), also known as query by image content (QBIC), and content-based visual information retrieval (CBVIR), are applications of computer vision to facilitate image retrieval and searching of digital images in large databases. “Content-based” image retrieval typically involves an analysis of the actual contents of an image (e.g., colors, shapes, textures and other information that can be derived from the image itself). Without the ability to examine image content, searches may rely on meta-data, such as captions and keywords, which may be laborious and expensive to produce. It is worth noting that Google introduced its “image labeler” feature, in the form of a game, which encourages users to label random images to help improve the quality of Google's meta-data based image search service.